Monthly Archives: October 2017

“Boo!” Is that a ghost or ghoul? No—it’s something much spookier: bad customer contact data. Did you know that less than half of retailers trust their data to make important business decisions? In fact, 57 percent of retailers say that they rely on educated guesses or gut feelings to make decisions based on their data. While blood and guts may have a place in horror movies, gut feelings are simply not enough to go on for important business decisions. Accurate, reliable data to drive decision-making is a far stronger retail strategy.

This week I attended the 50th Annual ISM show, which is the largest Health and Human Services (HHS) technology event of the year. Here, I had the opportunity to chat with some interesting people within the HHS space to understand their organization’s current data management practices. I was also able to listen to some informative sessions that explored ways to improve the HHS space using new, innovative technologies. I have personally spent the past couple years working in and learning about the HHS market, so I wanted to sit down and reflect on the most pressing topics in our world.

Businesses today continue to see data gaining in importance. As more and more organizations work to harness the power that their information can afford them, their underlying data is affecting every aspect of their operations. Departments like customer service, digital commerce, finance, compliance, operations, and more are all working to figure out how they can use data to better serve their customers, reduce risk, and become more efficient in their operations. As a result of these trends, an increasing number of businesses are looking to improve their data management practices.

Data conversions are tricky undertakings. There is no way around that. They are time-consuming, expensive, and can feel downright overwhelming. For many agencies in the public sector, the constraints on budgets and resources are a challenge day in and day out. When it comes to data conversions or modernization projects, there is no exception. Across organizations in the United States, approximately 80% of data migrations fail. Why, you ask? Let’s take a closer look.

Extract, transform, and load (ETL) is the process of integrating data from multiple, typically disparate, sources and bringing them together into one central location. It is a key component to businesses successfully making use of data in a data warehouse.

Sure, the process itself is fairly straightforward, and when done right, ETL prepares an organization for powerful business intelligence initiatives. However, a lot goes into a successful ETL process. Let’s discuss the three steps involved and why data management practices are an essential foundation to carrying ETL out properly.

In schoolyard terms, data migrations are the equivalent of the old “Telephone” game that you may have played as a kid. You get a line of people together, and the first person in the line whispers a sentence to the second person – “The quick brown fox jumped over the lazy dogs.” The second person then whispers this phrase to the next person, and so on, until they get to the end of the line. At that point, the last person says what the sentence is – in this case, “The slick clown’s socks slumped over the crazy bogs.” As you can see, the end result may be similar to the start, but it’s definitely not the same!

A single customer view is a consolidated, consistent, and holistic representation of the data a business possesses about each of its individual customers. It’s often discussed as a marketing tool, frequently in the context of retail customers or consumers. Yet having a robust single customer view has value to most medium or large businesses – those whose customer base is too large for any single person to know and understand. And it has value beyond the marketing department...

Bright and early on a Thursday morning, hundreds of data professionals convened for the 2017 Data Governance Financial Services (DGFS) conference. Hosted in Jersey City, New Jersey, DGFS brings together likeminded and passionate data professionals from across the country with a common mission: to share best practices and overcome challenges related to their data governance programs.

Have you ever met someone who made a great first impression, but the more you got to know them, the more flaws you noticed? Maybe they said one thing but did another. Or you expected them to show up at the designated place and time and they failed to show up. Or sometimes, they are just nothing like what you’d thought and you realized you made false assumptions. Well, sometimes your data can be the same way. At a first glance, it isn’t always immediately clear what issues may be lurking just past the surface within your data. That’s where data profiling comes in.

This past week I was lucky enough to attend Strata Data Conference. The conference allows big data's most influential business decision makers and strategists to gather in order to share experiences, thoughts, strategies, and products with the goal of positively impacting their business or technology. The event was held at the Javits Center in New York City. Placing the conference in the heart of NYC allows companies from Wall Street and Silicon Alley to attend with relative ease, ensuring all industries are tapping into the opportunity that Strata presents.